Data Analyst - Enterprise Risk

Hello. We’re Teya.

Teya was founded on a simple belief: local businesses deserve better.

They are the cafés, restaurants, salons, shops and entrepreneurs that bring character to our high streets, create jobs and keep communities moving. Yet for too long, financial services has made life harder for them - with clunky tools, poor support and complexity that gets in the way of running a business.

Teya exists to change that.

We’re building a financial platform for local businesses across Europe - one built around simple tools, thoughtful design and real human support. Our Members rely on us to help them run their business with confidence, and that responsibility shapes the way we work.

We move fast. We care about quality. We stay close to the detail. And we believe great performance and genuine hospitality should go hand in hand.

If you want to build meaningful products, solve real problems and make a genuine difference for local businesses, we’d love to hear from you

Senior Data Analyst – Enterprise Risk

Teya is building its Enterprise Risk Management Framework (ERMF) and strengthening how risk information is brought together, analysed and reported across the business.

As Senior Data Analyst, you will play a key role in turning risk data into reliable measures, meaningful insights and actionable reporting. You'll work closely with the Head of Enterprise & Operational Risk and stakeholders across Credit, Fraud, AML, People, IT, Operations and Finance to understand the risks facing the business, identify the right data to monitor them, and develop effective reporting and early-warning capabilities.

This is a business-facing analytical role. Understanding risk, working effectively with risk owners and translating their requirements into practical data solutions will be just as important as your technical skills. You'll help ensure key risk indicators (KRIs) are meaningful, data is reliable, thresholds are appropriately informed by analysis, and reporting supports better risk oversight and decision-making.

You'll also help modernise how risk reporting is produced, using AI models and workflow automation to bring together internal data and relevant external information, identify emerging issues and reduce manual effort. The focus is on practical application, with robust validation, traceability and human oversight.

This is an opportunity to combine risk expertise, analytical problem-solving and emerging AI capabilities to help shape how a growing fintech business identifies, monitors and manages risk.

• Develop risk data and reporting: Identify and map relevant data sources, using tools such as Snowflake to build repeatable datasets for KRIs across operational, credit, fraud, AML, people, capital, liquidity and other risk categories.
• Partner with risk stakeholders: Work closely with the Head of Enterprise & Operational Risk and risk owners across the business to understand their priorities, clarify reporting requirements and translate risk management needs into practical analytical solutions.
• Analyse and monitor risk indicators: Use SQL and Python to investigate trends, test assumptions, identify anomalies and understand changes in risk exposure. Help risk owners interpret findings and determine where further investigation or action may be needed.
• Support KRI definitions and thresholds: Work with risk stakeholders to validate indicator definitions, assess data quality and analyse historical and current performance to inform threshold calibration. Help ensure indicators provide meaningful insight into the risks they are intended to monitor.
• Build dashboards and management reporting: Develop clear, accessible dashboards using Tableau, Power BI or similar tools, supporting monthly ERMF reporting and management oversight.
• Automate risk reporting with AI: Build and improve AI-enabled workflows that bring together internal data and relevant external signals, including regulatory updates, market developments, news and emerging threats. Help generate evidence-based reporting drafts for review by the Head of Enterprise & Operational Risk and CRO.
• Develop early-warning capabilities: Help design and test approaches to monitor KRI movements and operational data throughout the month, highlighting unusual patterns and potential emerging risks with clear supporting evidence for investigation.
• Support risk technology and integration: Translate ERMF and reporting requirements into practical data, integration and workflow specifications for Teya's governance, risk and compliance (GRC) solution, partnering with Risk, Data and Technology colleagues on implementation.
• Improve data quality and governance: Document data sources, definitions, lineage, refresh frequencies, validation checks and limitations to ensure reporting is consistent, explainable and reliable.
• Drive continuous improvement: Identify opportunities to simplify reporting, improve analytical processes and strengthen how risk information is used across the business.
• Trusted risk data: KRI datasets are accurate, consistent, well-documented and refreshed at the required frequency.
• Meaningful risk insight: Analysis helps risk owners understand changes in risk exposure, investigate anomalies and make informed decisions.
• Effective business partnering: Risk stakeholders have confidence in the data, reporting and analysis provided, and their requirements are translated into practical solutions.
• Clear management reporting: Monthly ERMF reporting is timely, evidence-based and increasingly automated.
• Earlier identification of emerging risks: Explainable monitoring highlights significant movements and potential issues for investigation.
• Reliable AI-enabled workflows: Automation reduces manual effort while maintaining accuracy, traceability and appropriate human review.
• Stronger risk data foundations: Data definitions, quality checks and reporting processes support the continued development of Teya's ERMF and GRC capabilities.

• Experience in a data analytics, risk analytics or similar analytical role within a risk function, ideally covering enterprise, operational, credit, fraud or financial crime risk.
• A practical understanding of how risk is identified, assessed and monitored, including how risk appetite, controls, risk drivers, KRIs and thresholds connect.
• Strong business partnering skills, with the ability to work closely with risk owners and non-technical stakeholders, understand their needs and translate them into meaningful analysis, reporting and monitoring solutions.
• Working knowledge of SQL and Python for data analysis and practical automation. You don't need to be a software engineer, but you should be comfortable using these tools, including with AI assistance, and validating the accuracy of the results.
• Experience building dashboards and management reports using Tableau, Power BI or similar visualisation tools.
• Strong analytical judgement, with the ability to challenge assumptions, investigate unexpected results, assess data quality and communicate clearly what the data does—and does not—show.
• Experience working with data from multiple sources and improving the reliability, consistency and usability of analytical outputs.
• Hands-on curiosity about current AI models and AI-enabled workflow automation, with the ability to build and improve workflows that support data gathering, analysis and risk reporting.
• A structured, pragmatic approach to problem-solving, with the confidence to work through ambiguity and take ownership of deliverables.
• Experience in financial services, payments, fintech or another regulated environment.
• Exposure to enterprise risk management frameworks and risk appetite frameworks.
• Experience working with Snowflake or another cloud data warehouse.
• Familiarity with data pipelines, APIs, workflow orchestration or system integrations.
• Exposure to governance, risk and compliance (GRC) platforms.
• Experience with statistical analysis, anomaly detection, forecasting or early-warning indicators.
• Experience using external regulatory, market or threat intelligence to support risk monitoring.
• Familiarity with AI agents, large language models (LLMs) or AI-enabled workflow automation tools.

• Competitive salary: A competitive package aligned with your experience and impact.
• Equity participation: The opportunity to share in Teya's growth, subject to the applicable scheme and eligibility.
• Pension: Workplace pension provision.
• Holiday: Generous annual leave to support a healthy work-life balance.
• Flexible working: A flexible working environment built around collaboration, ownership and getting things done.
• Learning and development: Opportunities to develop your risk, data analytics and AI automation skills as the business grows.
• Health and wellbeing: Support for your wellbeing, with benefits designed to help you thrive at work and beyond.
• Team and company events: Opportunities to connect with colleagues, share ideas and celebrate success.

Teya is proud to be an equal opportunity employer.

We are committed to creating an inclusive environment where everyone regardless of race, ethnicity, gender identity or expression, sexual orientation, age, disability, religion, or background can thrive and do their best work. We believe that a diverse team leads to better ideas, stronger outcomes, and a more supportive workplace for all.

If you require any reasonable adjustments at any stage of the recruitment process whether for interviews, assessments, or other parts of the application—we encourage you to let us know. We are committed to ensuring that every candidate has a fair and accessible experience with us.